Scoop watch

A real example: what you get

Example in Astrophysics Computer science and AI Biology and medicine

3 new papers close to the project “Antibiotic Exposure and the Response to Anti-PD-1 Therapy in Advanced Melanoma: A Retrospective Cohort Study”, found in the announcements of its first days

How close it is on each facet: question, method, data, results
What it shares with your project and what stays your own
What to do: cite it, set your project apart, contact the authors, hurry
The prior art of the last three years, and the new papers of the teams you name

Partial overlap

Both studies examine factors associated with outcomes of PD-1 blockade in melanoma using observational patient data. This paper evaluates tumour transcriptional states and disease control rather than antibiotic exposure, objective response and overall survival.

Glycolysis and interferon transcription in melanoma immunotherapy

What stays your own

The project's contribution remains its assessment of systemic antibiotic exposure around treatment initiation in a three-hospital cohort, with adjustment for clinical prognostic factors. Distinguish this clinical exposure analysis from transcriptomic biomarker studies, and avoid presenting either study as direct evidence that antibiotics impair treatment through the gut microbiome.

The email you would have received · 3 new papers are close to your projects

The first results

Glycolysis and interferon transcription in melanoma immunotherapy Partial overlap Both studies examine factors associated with outcomes of PD-1 blockade in melanoma using observational patient data. This paper evaluates tumour transcriptional states and disease control rather than antibiotic exposure, objective response and overall survival.
Antibiotics drive immunometabolic rewiring of human immune responses Partial overlap The paper shows that certain antibiotics can impair human T-cell activation and expansion, providing a potential biological explanation for an association between antibiotic exposure and poorer anti-PD-1 outcomes. It also suggests that antibiotic effects on immunity need not be mediated …

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How it works

Describe your project in a few paragraphs (question, method, data, expected results), or upload the PDF of your draft: an AI writes the description from it.
At once, the papers of the last 3 years are searched on arXiv, in the journals and on OpenReview, with those that already cite your key references: you see what is already published close to your project.
Every day, the new papers are compared with each facet of your project apart (question, method, data, results), and with the names that identify it. An AI reads the closest abstracts.
When one overlaps your project, you get an email: a strong overlap (same question, same or very close approach) or a partial one, with how close it is on each facet.
For each paper, the AI says what it shares with your project, what stays yours and what to do: cite it, set your project apart, contact the authors or publish soon. A strong overlap is compared with the full text of the paper when its license allows it.

Follow competing teams, and give your key references: their new papers, and the papers that cite your references, are always read.
Mark a paper as not relevant and similar ones are pushed down; add the close ones to a collection.
Your own papers are never reported: give your name and those of your usual coauthors.
Up to 5 projects watched.
Your description stays private and is only used for the comparison. A PDF is read once and is not stored, unless you ask us to keep its text for the full-text comparisons.

Useful during a thesis or a long project, to know early that a competing team is close, cite it, and adjust how you present your work.